Biology subjects

Patra, S.

Publications and source records attributed to Patra, S..

2 recordsLinked to original sources

bioRxiv2018

Short-term aerobic training does not improve memory functioning in relapsing remitting multiple sclerosis - a randomized controlled trial

BackgroundOnly few aerobic exercise intervention trials specifically targeting cognitive functioning have been performed in MS.\n\nObjective and methodsThis randomized controlled trial aimed to determine the effects of aerobic exercise on cognition in relapsing-remitting MS. The primary outcome was verbal memory (Verbal learning and memory test, VLMT). Patients were randomized to an intervention group (IG) program or a waitlist control group (CG). Patients in the IG exercised according to an individually tailored training schedule (with 2-3 sessions per week for 12 weeks). The primary analysis was carried out using the intention-to-treat (ITT) sample with ANCOVA adjusting for baseline scores.\n\nResults77 RRMS patients were screened and 68 participants randomized (CG n=34; IG n=34). The sample comprised 68% females, had a mean age of 39 years, a mean disease duration of 6.3 years, and a mean EDSS of 1.8. No significant effects were detected in the ITT analysis for the primary endpoint VLMT or any other cognitive measures. Moreover, no significant treatment effects were observed for quality of life, fatigue, or depressive symptoms.\n\nConclusionThis study failed to demonstrate beneficial effects of aerobic exercise on cognition in RRMS.\n\nThe trial was prospectively registered at clinicaltrials.gov (NCT02005237).

clinical trials↗

bioRxiv2018

Discovery of Large Disjoint Motif in Biological Network using Dynamic Expansion Tree

Network motifs play an important role in structural analysis of biological networks. Identification of such network motifs leads to many important applications, such as: understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families etc. However, identification of network motifs is challenging as it involved graph isomorphism which is computationally hard problem. Though this problem has been studied extensively in the literature using different computational approaches, we are far from encouraging results. Motivated by the challenges involved in this field we have proposed an efficient and scalable Motif discovery algorithm using a Dynamic Expansion Tree (MDET). In this algorithm embeddings corresponding to child node of expansion tree are obtained from the embeddings of parent node, either by adding a vertex with time complexity O(n) or by adding an edge with time complexity O(1) without involving any isomorphic check. The growth of Dynamic Expansion Tree (DET) depends on availability of patterns in the target network. DET reduces space complexity significantly and the memory limitation of static expansion tree can overcome. The proposed algorithm has been tested on Protein Protein Interaction (PPI) network obtained from MINT database. It is able to identify large motifs faster than most of the existing motif discovery algorithms.

bioinformatics↗